ML is a more specific subset of AI. It works on the idea that machines can “think” for themselves and learn without our constant supervision.
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After rolling out a slew of new services at last year's re:Invent conference, AWS is polishing them up before this year's big event.
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A quantum version of the building block behind neural networks could be exponentially more powerful.
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A large-scale genetic project is currently underway in Nevada that's using advanced analytics and machine learning to identify connections between
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Whether you build (in-house) or buy (vendor), your software should specialize in identifying gaps in your data.
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Darktrace’s unsupervised-learning models sound the alarm before intruders can cause serious damage.
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While efficiency and risk analysis are the top use cases today, the data center industry is only scratching the surface of what will be possible in the future.
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Even the best AI tools are more artistry than science, require reams of carefully curated data from which they inherit significant biases, cannot generalize beyond their domains and are black boxes that even their creators don’t fully understand. In short, they are pattern extractors, not magic.
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Machine learning can also go terribly wrong, making you regret that enthusiastic rush to adopt
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